Diagnostic Accuracy and Interobserver Reliability of Rotator Cuff Tear Detection With Ultrasonography Are Improved With Attentional Deep Learning
The attentional deep learning model aids physicians in improving the accuracy and consistency of ultrasound diagnosis of torn or intact rotator cuff tendons.
Authors
- Wen‐Yi Chou (ORCID: https://orcid.org/0000-0002-8400-3457)
- Kuan-Ting Wu (ORCID: https://orcid.org/0000-0003-3953-7860)
- Jenn-Jier James Lien (ORCID: https://orcid.org/0000-0003-3944-7523)
- Po‐Cheng Chen (ORCID: https://orcid.org/0000-0002-6386-2850)
- Ching-Di Chang (ORCID: https://orcid.org/0000-0001-6683-0013)
Institutions
- Chang Gung University (TW)
- Kaohsiung Chang Gung Memorial Hospital (TW)
- Kaohsiung Municipal Ta-Tung Hospital (TW)
- Kaohsiung Municipal Hsiao-Kang Hospital (TW)
- National Cheng Kung University (TW)
- Cheng Shiu University (TW)
Publication Details
- Journal
- Arthroscopy The Journal of Arthroscopic and Related Surgery
- Published
- 2024-12-24
- DOI
- https://doi.org/10.1016/j.arthro.2024.12.024
- Citations
- 3
- Primary Topic
- Advanced Sensor Technologies Research
- Type
- article
- Field-Weighted Citation Impact
- 0.55